weather_plots.Rout

R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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[Previously saved workspace restored]

> # Multiple plot function
> #
> # ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
> # - cols:   Number of columns in layout
> # - layout: A matrix specifying the layout. If present, 'cols' is ignored.
> #
> # If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
> # then plot 1 will go in the upper left, 2 will go in the upper right, and
> # 3 will go all the way across the bottom.
> #
> multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
+   require(grid)
+   
+   # Make a list from the ... arguments and plotlist
+   plots <- c(list(...), plotlist)
+   
+   numPlots = length(plots)
+   
+   # If layout is NULL, then use 'cols' to determine layout
+   if (is.null(layout)) {
+     # Make the panel
+     # ncol: Number of columns of plots
+     # nrow: Number of rows needed, calculated from # of cols
+     layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
+                      ncol = cols, nrow = ceiling(numPlots/cols))
+   }
+   
+   if (numPlots==1) {
+     print(plots[[1]])
+     
+   } else {
+     # Set up the page
+     grid.newpage()
+     pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
+     
+     # Make each plot, in the correct location
+     for (i in 1:numPlots) {
+       # Get the i,j matrix positions of the regions that contain this subplot
+       matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
+       
+       print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
+                                       layout.pos.col = matchidx$col))
+     }
+   }
+ }
> 
> library(grid);library(ggplot2);library(scales)
> 
> weather_daily <- read.csv("weather_daily.csv")
> weather_daily$date = as.Date(as.character(weather_daily$date),"%Y-%m-%d")
> weather_daily$boug = format(weather_daily$date, format="%b %d")           
> 
> weather_daily_recent = weather_daily[weather_daily$date >= Sys.Date(),]
> weather_daily_recent$rain_adj <- NA
> 
> weather_daily_recent[weather_daily_recent$rain_in > 0,]$rain_adj <- weather_daily_recent[weather_daily_recent$rain_in > 0,]$high_f
> 
> p1 <- ggplot(data=weather_daily_recent[weather_daily_recent$city=='Washington',])+
+   geom_line(aes(x=date,y=low_f,group=city,colour='Low'),stat='identity')+
+   geom_line(aes(x=date,y=high_f,group=city,colour='High'))+
+   geom_point(aes(x=date,y=rain_adj,group=city,colour='Rain',size=rain_in))+
+   scale_y_continuous(limits=c(min(weather_daily_recent$low_f)-10,max(weather_daily_recent$high_f)+10)
+                      ,breaks=seq(0,110,5))+
+   ggtitle('Upcoming Forecast for Washington DC')+
+   xlab("Date")+
+   ylab("Tempature")+
+    scale_x_date(breaks = date_breaks('days'),labels = date_format("%b %d"))
>  
> p2 <- ggplot(data=weather_daily_recent[weather_daily_recent$city=='New_York',])+
+   geom_line(aes(x=date,y=low_f,group=city,colour='Low'))+
+   geom_line(aes(x=date,y=high_f,group=city,colour='High'))+
+   geom_point(aes(x=date,y=rain_adj,group=city,colour='Rain',size=rain_in))+
+   scale_y_continuous(limits=c(min(weather_daily_recent$low_f)-10,max(weather_daily_recent$high_f)+10)
+                      ,breaks=seq(0,110,5))+
+   ggtitle('Upcoming Forecast for New York, NY')+
+   xlab("Date")+
+   ylab("Tempature")+
+   scale_x_date(breaks = date_breaks('days'),labels = date_format("%b %d"))
> 
> 
> p3 <- ggplot(data=weather_daily_recent[weather_daily_recent$city=='Liverpool',])+
+   geom_line(aes(x=date,y=low_f,group=city,colour='Low'))+
+   geom_line(aes(x=date,y=high_f,group=city,colour='High'))+
+   geom_point(aes(x=date,y=rain_adj,group=city,colour='Rain',size=rain_in))+
+   scale_y_continuous(limits=c(min(weather_daily_recent$low_f)-10,max(weather_daily_recent$high_f)+10)
+                      ,breaks=seq(0,110,5))+
+   ggtitle('Upcoming Forecast for Liverpool, England')+
+   xlab("Date")+
+   ylab("Tempature")+
+   scale_x_date(breaks = date_breaks('days'),labels = date_format("%b %d"))
> 
> p4 <- ggplot(data=weather_daily_recent[weather_daily_recent$city=='Sydney',])+
+   geom_line(aes(x=date,y=low_f,group=city,colour='Low'))+
+   geom_line(aes(x=date,y=high_f,group=city,colour='High'))+
+   geom_point(aes(x=date,y=rain_adj,group=city,colour='Rain',size=rain_in))+
+   scale_y_continuous(limits=c(min(weather_daily_recent$low_f)-10,max(weather_daily_recent$high_f)+10)
+                      ,breaks=seq(0,110,5))+
+   ggtitle('Upcoming Forecast for Sydney, Australia')+
+   xlab("Date")+
+   ylab("Tempature")+
+   scale_x_date(breaks = date_breaks('days'),labels = date_format("%b %d"))
> 
> p5 <- multiplot(p1, p2, p3, p4, cols=2)
Warning messages:
1: Removed 6 rows containing missing values (geom_point). 
2: Removed 5 rows containing missing values (geom_point). 
3: Removed 9 rows containing missing values (geom_point). 
4: Removed 10 rows containing missing values (geom_point). 
> 
> 
> png(paste('weather_daily_',Sys.Date(),'.png',sep=''),width =900,height =580)
> multiplot(p1, p2, p3, p4, cols=2)
Warning messages:
1: Removed 6 rows containing missing values (geom_point). 
2: Removed 5 rows containing missing values (geom_point). 
3: Removed 9 rows containing missing values (geom_point). 
4: Removed 10 rows containing missing values (geom_point). 
> dev.off()
pdf 
  2 
> 
> 
> proc.time()
   user  system elapsed 
  4.777   0.163   5.167